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Markov Switching Garch Models In R
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Book Synopsis Markov-Switching GARCH Models in R by : David Ardia
Download or read book Markov-Switching GARCH Models in R written by David Ardia and published by . This book was released on 2019 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: We describe the package MSGARCH, which implements Markov-switching GARCH models in R with efficient C++ object-oriented programming. Markov-switching GARCH models have become popular methods to account for regime changes in the conditional variance dynamics of time series. The package MSGARCH allows the user to perform simulations as well as Maximum Likelihood and MCMC/Bayesian estimations of a very large class of Markov-switching GARCH-type models. The package also provides methods to make single-step and multi-step ahead forecasts of the complete conditional density of the variable of interest. Risk management tools to estimate conditional volatility, Value-at-Risk, and Expected-Shortfall are also available. We illustrate the broad functionality of the MSGARCH package using exchange rate and stock market return data.
Book Synopsis Modelling Volatility with Markov-switching GARCH Models by : María Ferrer Fernández
Download or read book Modelling Volatility with Markov-switching GARCH Models written by María Ferrer Fernández and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Financial Risk Management with Bayesian Estimation of GARCH Models by : David Ardia
Download or read book Financial Risk Management with Bayesian Estimation of GARCH Models written by David Ardia and published by Springer Science & Business Media. This book was released on 2008-05-08 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents in detail methodologies for the Bayesian estimation of sing- regime and regime-switching GARCH models. These models are widespread and essential tools in n ancial econometrics and have, until recently, mainly been estimated using the classical Maximum Likelihood technique. As this study aims to demonstrate, the Bayesian approach o ers an attractive alternative which enables small sample results, robust estimation, model discrimination and probabilistic statements on nonlinear functions of the model parameters. The author is indebted to numerous individuals for help in the preparation of this study. Primarily, I owe a great debt to Prof. Dr. Philippe J. Deschamps who inspired me to study Bayesian econometrics, suggested the subject, guided me under his supervision and encouraged my research. I would also like to thank Prof. Dr. Martin Wallmeier and my colleagues of the Department of Quantitative Economics, in particular Michael Beer, Roberto Cerratti and Gilles Kaltenrieder, for their useful comments and discussions. I am very indebted to my friends Carlos Ord as Criado, Julien A. Straubhaar, J er ^ ome Ph. A. Taillard and Mathieu Vuilleumier, for their support in the elds of economics, mathematics and statistics. Thanks also to my friend Kevin Barnes who helped with my English in this work. Finally, I am greatly indebted to my parents and grandparents for their support and encouragement while I was struggling with the writing of this thesis.
Book Synopsis A New Approach to Markov-Switching GARCH Models by : Markus Haas
Download or read book A New Approach to Markov-Switching GARCH Models written by Markus Haas and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of Markov-switching models to capture the volatility dynamics of financial time series has grown considerably during past years, in part because they give rise to a plausible interpretation of nonlinearities. Nevertheless, GARCH-type models remain ubiquitous in order to allow for nonlinearities associated with time-varying volatility. Existing methods of combining the two approaches are unsatisfactory, as they either suffer from severe estimation difficulties or else their dynamic properties are not well understood. In this article we present a new Markov-switching GARCH model that overcomes both of these problems. Dynamic properties are derived and their implications for the volatility process discussed. We argue that the disaggregation of the variance process offered by the new model is more plausible than in the existing variants. The approach is illustrated with several exchange rate return series. The results suggest that a promising volatility model is an independent switching GARCH process with a possibly skewed conditional mixture density.
Book Synopsis Advances in Markov-Switching Models by : James D. Hamilton
Download or read book Advances in Markov-Switching Models written by James D. Hamilton and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of state-of-the-art papers on the properties of business cycles and financial analysis. The individual contributions cover new advances in Markov-switching models with applications to business cycle research and finance. The introduction surveys the existing methods and new results of the last decade. Individual chapters study features of the U. S. and European business cycles with particular focus on the role of monetary policy, oil shocks and co movements among key variables. The short-run versus long-run consequences of an economic recession are also discussed. Another area that is featured is an extensive analysis of currency crises and the possibility of bubbles or fads in stock prices. A concluding chapter offers useful new results on testing for this kind of regime-switching behaviour. Overall, the book provides a state-of-the-art over view of new directions in methods and results for estimation and inference based on the use of Markov-switching time-series analysis. A special feature of the book is that it includes an illustration of a wide range of applications based on a common methodology. It is expected that the theme of the book will be of particular interest to the macroeconomics readers as well as econometrics professionals, scholars and graduate students. We wish to express our gratitude to the authors for their strong contributions and the reviewers for their assistance and careful attention to detail in their reports.
Download or read book GARCH Models written by Christian Francq and published by John Wiley & Sons. This book was released on 2019-03-19 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides a comprehensive and updated study of GARCH models and their applications in finance, covering new developments in the discipline This book provides a comprehensive and systematic approach to understanding GARCH time series models and their applications whilst presenting the most advanced results concerning the theory and practical aspects of GARCH. The probability structure of standard GARCH models is studied in detail as well as statistical inference such as identification, estimation, and tests. The book also provides new coverage of several extensions such as multivariate models, looks at financial applications, and explores the very validation of the models used. GARCH Models: Structure, Statistical Inference and Financial Applications, 2nd Edition features a new chapter on Parameter-Driven Volatility Models, which covers Stochastic Volatility Models and Markov Switching Volatility Models. A second new chapter titled Alternative Models for the Conditional Variance contains a section on Stochastic Recurrence Equations and additional material on EGARCH, Log-GARCH, GAS, MIDAS, and intraday volatility models, among others. The book is also updated with a more complete discussion of multivariate GARCH; a new section on Cholesky GARCH; a larger emphasis on the inference of multivariate GARCH models; a new set of corrected problems available online; and an up-to-date list of references. Features up-to-date coverage of the current research in the probability, statistics, and econometric theory of GARCH models Covers significant developments in the field, especially in multivariate models Contains completely renewed chapters with new topics and results Handles both theoretical and applied aspects Applies to researchers in different fields (time series, econometrics, finance) Includes numerous illustrations and applications to real financial series Presents a large collection of exercises with corrections Supplemented by a supporting website featuring R codes, Fortran programs, data sets and Problems with corrections GARCH Models, 2nd Edition is an authoritative, state-of-the-art reference that is ideal for graduate students, researchers, and practitioners in business and finance seeking to broaden their skills of understanding of econometric time series models.
Download or read book GARCH Models written by Christian Francq and published by John Wiley & Sons. This book was released on 2011-06-24 with total page 469 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive and systematic approach to understanding GARCH time series models and their applications whilst presenting the most advanced results concerning the theory and practical aspects of GARCH. The probability structure of standard GARCH models is studied in detail as well as statistical inference such as identification, estimation and tests. The book also provides coverage of several extensions such as asymmetric and multivariate models and looks at financial applications. Key features: Provides up-to-date coverage of the current research in the probability, statistics and econometric theory of GARCH models. Numerous illustrations and applications to real financial series are provided. Supporting website featuring R codes, Fortran programs and data sets. Presents a large collection of problems and exercises. This authoritative, state-of-the-art reference is ideal for graduate students, researchers and practitioners in business and finance seeking to broaden their skills of understanding of econometric time series models.
Book Synopsis Theory and Inference for a Markov Switching GARCH Model by : Luc Bauwens
Download or read book Theory and Inference for a Markov Switching GARCH Model written by Luc Bauwens and published by . This book was released on 2007 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Macroeconometrics and Time Series Analysis by : Steven Durlauf
Download or read book Macroeconometrics and Time Series Analysis written by Steven Durlauf and published by Springer. This book was released on 2016-04-30 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: Specially selected from The New Palgrave Dictionary of Economics 2nd edition, each article within this compendium covers the fundamental themes within the discipline and is written by a leading practitioner in the field. A handy reference tool.
Book Synopsis An Implementation of Markov Regime Switching GARCH Models in Matlab by : Thomas Chuffart
Download or read book An Implementation of Markov Regime Switching GARCH Models in Matlab written by Thomas Chuffart and published by . This book was released on 2017 with total page 9 pages. Available in PDF, EPUB and Kindle. Book excerpt: MSGtool is a MATLAB toolbox which provides a collection of functions for the simulation and estimation of a large variety of Markov Switching GARCH (MSG) models. Currently, the software integrates a method to select the best starting values for the estimation and a post-estimation analysis to ensure the convergence. The toolbox is very flexible a user-friendly with a large number possible options. In this paper, we give some illustrative examples.
Book Synopsis Evaluating Specification Tests for Markov-Switching Time Series Models by : Daniel R. Smith
Download or read book Evaluating Specification Tests for Markov-Switching Time Series Models written by Daniel R. Smith and published by . This book was released on 2007 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt: We evaluate the performance of several specification tests for Markov regime-switching time series models. We consider the Lagrange Multiplier and dynamic specification tests of Hamilton (1994) and Ljung-Box tests based on both the generalized residual and a standard-normal residual constructed using the Rosenblatt transformation. The size and power of the tests is studied using Monte Carlo experiments. We find that the LM tests have the best size and power properties. The Ljung-Box tests exhibit slight size distortions, though the tests based on the Roenblatt transformation perform better than the generalized residual-based tests. The tests exhibit impressive power to detect both autocorrelation and ARCH. The tests are illustrated with a Markov-Switching GARCH model fitted to the US Dollar-British Pound exchange rate, finding that both autocorrelation and GARCH effects are needed to adequately fit the data.
Book Synopsis Marginal Likelihood for Markov-switching and Change-point GARCH Models by : Luc Bauwens
Download or read book Marginal Likelihood for Markov-switching and Change-point GARCH Models written by Luc Bauwens and published by . This book was released on 2011 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Beyond Traditional Probabilistic Methods in Economics by : Vladik Kreinovich
Download or read book Beyond Traditional Probabilistic Methods in Economics written by Vladik Kreinovich and published by Springer. This book was released on 2018-11-24 with total page 1157 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent research on probabilistic methods in economics, from machine learning to statistical analysis. Economics is a very important – and at the same a very difficult discipline. It is not easy to predict how an economy will evolve or to identify the measures needed to make an economy prosper. One of the main reasons for this is the high level of uncertainty: different difficult-to-predict events can influence the future economic behavior. To make good predictions and reasonable recommendations, this uncertainty has to be taken into account. In the past, most related research results were based on using traditional techniques from probability and statistics, such as p-value-based hypothesis testing. These techniques led to numerous successful applications, but in the last decades, several examples have emerged showing that these techniques often lead to unreliable and inaccurate predictions. It is therefore necessary to come up with new techniques for processing the corresponding uncertainty that go beyond the traditional probabilistic techniques. This book focuses on such techniques, their economic applications and the remaining challenges, presenting both related theoretical developments and their practical applications.
Book Synopsis Maximum Likelihood Estimation of the Markov-Switching GARCH Model by : Maciej Augustyniak
Download or read book Maximum Likelihood Estimation of the Markov-Switching GARCH Model written by Maciej Augustyniak and published by . This book was released on 2016 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Markov-switching GARCH model offers rich dynamics to model financial data. Estimating this path dependent model is a challenging task because exact computation of the likelihood is infeasible in practice. This difficulty led to estimation procedures either based on a simplification of the model or not dependent on the likelihood. There is no method available to obtain the maximum likelihood estimator without resorting to a modification of the model. A novel approach is developed based on both the Monte Carlo expectation-maximization algorithm and importance sampling to calculate the maximum likelihood estimator and asymptotic variance-covariance matrix of the Markov-switching GARCH model. Practical implementation of the proposed algorithm is discussed and its effectiveness is demonstrated in simulation and empirical studies.
Book Synopsis State-space Models with Regime Switching by : Chang-Jin Kim
Download or read book State-space Models with Regime Switching written by Chang-Jin Kim and published by Mit Press. This book was released on 1999 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Both state-space models and Markov switching models have been highly productive paths for empirical research in macroeconomics and finance. This book presents recent advances in econometric methods that make feasible the estimation of models that have both features. One approach, in the classical framework, approximates the likelihood function; the other, in the Bayesian framework, uses Gibbs-sampling to simulate posterior distributions from data.The authors present numerous applications of these approaches in detail: decomposition of time series into trend and cycle, a new index of coincident economic indicators, approaches to modeling monetary policy uncertainty, Friedman's "plucking" model of recessions, the detection of turning points in the business cycle and the question of whether booms and recessions are duration-dependent, state-space models with heteroskedastic disturbances, fads and crashes in financial markets, long-run real exchange rates, and mean reversion in asset returns.
Book Synopsis Hidden Markov Models for Time Series by : Walter Zucchini
Download or read book Hidden Markov Models for Time Series written by Walter Zucchini and published by CRC Press. This book was released on 2017-12-19 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hidden Markov Models for Time Series: An Introduction Using R, Second Edition illustrates the great flexibility of hidden Markov models (HMMs) as general-purpose models for time series data. The book provides a broad understanding of the models and their uses. After presenting the basic model formulation, the book covers estimation, forecasting, decoding, prediction, model selection, and Bayesian inference for HMMs. Through examples and applications, the authors describe how to extend and generalize the basic model so that it can be applied in a rich variety of situations. The book demonstrates how HMMs can be applied to a wide range of types of time series: continuous-valued, circular, multivariate, binary, bounded and unbounded counts, and categorical observations. It also discusses how to employ the freely available computing environment R to carry out the computations. Features Presents an accessible overview of HMMs Explores a variety of applications in ecology, finance, epidemiology, climatology, and sociology Includes numerous theoretical and programming exercises Provides most of the analysed data sets online New to the second edition A total of five chapters on extensions, including HMMs for longitudinal data, hidden semi-Markov models and models with continuous-valued state process New case studies on animal movement, rainfall occurrence and capture-recapture data
Book Synopsis Maximum Likelihood Estimation of the Markov-Switching GARCH Model Based on a General Collapsing Procedure by : Maciej Augustyniak
Download or read book Maximum Likelihood Estimation of the Markov-Switching GARCH Model Based on a General Collapsing Procedure written by Maciej Augustyniak and published by . This book was released on 2017 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Markov-switching GARCH model allows for a GARCH structure with time-varying parameters. This flexibility is unfortunately undermined by a path dependence problem which complicates the parameter estimation process. This problem led to the development of computationally intensive estimation methods and to simpler techniques based on an approximation of the model, known as collapsing procedures. This article develops an original algorithm to conduct maximum likelihood inference in the Markov-switching GARCH model, generalizing and improving previously proposed collapsing approaches. A new relationship between particle filtering and collapsing procedures is established which reveals that this algorithm corresponds to a deterministic particle filter. Simulation and empirical studies show that the proposed method allows for a fast and accurate estimation of the model.
